How does TensorRT leverage attention masks to speed up inference ?
Open
Nobody has claimed this yet.
triaged
- Dominant language
- C++
- Stars
- 13.4k
- Forks
- 2.4k
- Avg merge
- 5d 3h
- Merged PRs (30d)
- 2
Description
Hello team,
Thanks for all the great work,
I am training a model where I am providing tile-wise constant attention masks (see picture below).
At inference time, how will TensorRT leverage this type of attention mask to speed up inference ?
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue names no file, test, or entry point to inspect. Start by identifying the TensorRT attention-mask and inference documentation or implementation area relevant to tile-wise constant masks. Done means establishing, with maintainer guidance, whether this mask pattern is leveraged for inference speedup and documenting the supported behavior.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp
- Domain
- machine-learning, performance
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100